Herein,we report a novel and highly efficient method for the synthesis ofα-phosphoryloxy carbonyl compounds via Rucatalyzed P(O)O–H insertion reactions of sulfoxonium ylides and phosphinic acids,with the assistance ...Herein,we report a novel and highly efficient method for the synthesis ofα-phosphoryloxy carbonyl compounds via Rucatalyzed P(O)O–H insertion reactions of sulfoxonium ylides and phosphinic acids,with the assistance of high-throughput experimentation(HTE)and machine learning(ML).A variety of P(O)O−H derivatives,including diarylphosphates,alkyl phosphates,and alkoxyphosphates,are competent candidates to react with sulfoxonium ylides in this transformation,and variousα-phosphoryloxy carbonyls and propylene phosphates are directly constructed.This approach utilizes readily available sulfoxonium ylide as a carbene precursor,and features mild conditions,operational simplicity,and broad functional groups tolerance,and could be used for late-stage functionalization of structurally complex bioactive molecules.Moreover,a conducive exploration of the reaction space is also conducted(756 reactions)and a machine learning model for reaction yield prediction has been developed and applied,showcasing the practical application of this newly workflow(HTE-ML)in the field of synthetic chemistry.展开更多
The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explore...The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.展开更多
Sm–Co-based films play an irreplaceable role in special applications due to their high curie temperature and magnetocrystalline anisotropic energy,especially in heat-assisted magnetic recording(HAMR),but the complex ...Sm–Co-based films play an irreplaceable role in special applications due to their high curie temperature and magnetocrystalline anisotropic energy,especially in heat-assisted magnetic recording(HAMR),but the complex composition of Sm–Co phase and unclear synergistic coupling mechanisms of multi-elemental doping become the challenges to enhance the properties.In this work,a novel strategy combining magnetron sputtering and a high-throughput experiment method is applied to solve the above-mentioned problems.Fe/Cu co-doping highly increases the remanence while maintaining a coercivity larger than 26 kOe,leading to an enhancement of the magnetic energy product to 18.1 MGOe.X-ray diffraction(XRD)and high-resolution transmission electron microscope(HRTEM)reveals that SmCo_(5) phase occupies the major fraction,with Co atoms partially substituted by Fe and Cu atoms.In situ Lorentz transmission electron microscopy(LTEM)observations show that the Sm(Co,Cu)5 phase effectively prohibits domain wall motions,leading to an increase of coercivity(H_(c)).Fe doping increases the low saturation magnetization(M_(s))and low remanence(Mr)due to the Fe atom having a higher saturation magnetic moment.The magnetization reversal behaviors are further verified by micromagnetic simulations.Our results suggest that Sm–Co-based films prepared via Fe/Cu co-doping could be a promising candidate for high-performed HAMR in the future.展开更多
The development of new engineering alloy chemistries and heat treatments is a time-consuming and iterative process.Here,a hybrid approach of the high-throughput precipitation simulations and decisive experiments is de...The development of new engineering alloy chemistries and heat treatments is a time-consuming and iterative process.Here,a hybrid approach of the high-throughput precipitation simulations and decisive experiments is developed to optimize the composition and manipulate the microstructure of Al-Zn-Mg-Cu alloys to achieve the expected yield strength and elongation.For that purpose,a multi-class Kampmann-Wagner numerical(KWN)framework is established and the contributions to precipitation kinetics and strength from primary phases and precipitates formed before age hardening are introduced for the first time.The composition/process-structure-property relationship of Al-Zn-Mg-Cu alloys is pre-sented and discussed in detail.Coupled with thermodynamic calculations,two concentration-optimized Al-Zn-Mg-Cu alloys with expected high yield strength and long elongation are designed,prepared,and characterized.The excellent strength and elongation of the designed alloys and the good agreement between the measured and model-predicted mechanical properties for these two alloys underscores the remarkable predictive power of the presently developed material design strategy.This work establishes a novel material design strategy for rapidly exploring the compositional space and investigating the effects of composition and heat treatment on the microstructure and performance of ultrahigh strength Al alloys and other materials.展开更多
Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,exp...Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.展开更多
Ti alloys,as leading lightweight and high-strength metallic materials,exhibit significant application potential in aerospace,marine engineering,biomedical,and other industries.However,the lack of fundamental understan...Ti alloys,as leading lightweight and high-strength metallic materials,exhibit significant application potential in aerospace,marine engineering,biomedical,and other industries.However,the lack of fundamental understanding of the microstructure−property relationship results in prolonged research and development(R&D)cycles,hindering the optimization of the performance of Ti alloys.Recently,the advent of high-throughput experimental(HTE)technology has shown promise in facilitating the efficient and demand-driven development of next-generation Ti alloys.This work reviews the latest advancements in HTE technology for Ti alloys.The high-throughput preparation(HTP)techniques commonly used in the fabrication of Ti alloys are addressed,including diffusion multiple,additive manufacturing(AM),vapor deposition and others.The current applications of high-throughput characterization(HTC)techniques in Ti alloys are shown.Finally,the research achievements in HTE technology for Ti alloys are summarized and the challenges faced in their industrial application are discussed.展开更多
Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is...Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.展开更多
In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
Cardiac injury initiates repair mechanisms and results in cardiac remodeling and fi-brosis,which appears to be a leading cause of cardiovascular diseases.Cardiac fi-brosis is characterized by the accumulation of extra...Cardiac injury initiates repair mechanisms and results in cardiac remodeling and fi-brosis,which appears to be a leading cause of cardiovascular diseases.Cardiac fi-brosis is characterized by the accumulation of extracellular matrix proteins,mainly collagen in the cardiac interstitium.Many experimental studies have demonstrated that fibrotic injury in the heart is reversible;therefore,it is vital to understand differ-ent molecular mechanisms that are involved in the initiation,progression,and resolu-tion of cardiac fibrosis to enable the development of antifibrotic agents.Of the many experimental models,one of the recent models that has gained renewed interest is isoproterenol(ISP)-induced cardiac fibrosis.ISP is a synthetic catecholamine,sympa-thomimetic,and nonselectiveβ-adrenergic receptor agonist.The overstimulated and sustained activation ofβ-adrenergic receptors has been reported to induce biochemi-cal and physiological alterations and ultimately result in cardiac remodeling.ISP has been used for decades to induce acute myocardial infarction.However,the use of low doses and chronic administration of ISP have been shown to induce cardiac fibrosis;this practice has increased in recent years.Intraperitoneal or subcutaneous ISP has been widely used in preclinical studies to induce cardiac remodeling manifested by fibrosis and hypertrophy.The induced oxidative stress with subsequent perturbations in cellular signaling cascades through triggering the release of free radicals is consid-ered the initiating mechanism of myocardial fibrosis.ISP is consistently used to induce fibrosis in laboratory animals and in cardiomyocytes isolated from animals.In recent years,numerous phytochemicals and synthetic molecules have been evaluated in ISP-induced cardiac fibrosis.The present review exclusively provides a comprehensive summary of the pathological biochemical,histological,and molecular mechanisms of ISP in inducing cardiac fibrosis and hypertrophy.It also summarizes the application of this experimental model in the therapeutic evaluation of natural as well as syn-thetic compounds to demonstrate their potential in mitigating myocardial fibrosis and hypertrophy.展开更多
Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism rem...Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.展开更多
In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-b...In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-based alloy(typically pure lead or lead-bismuth eutectic(LBE))is used as the coolant.To clarify the pressure build-up characteristics under water-jet injection,this study conducted several experiments by injecting pressurized water into a molten LBE pool at Sun Yat-sen University.To obtain a further understanding,several new experimental parameters were adopted,including the melt temperature,water subcooling,injection pressure,injection duration,and nozzle diameter.Through detailed analyses,it was found that the pressure and temperature during the water-melt interaction exhibited a consistent variation trend with our previous water-droplet injection mode LBE experiment.Similarly,the existence of a steam explosion was confirmed,which typically results in a much stronger pressure build-up.For the non-explosion cases,increasing the injection pressure,melt-pool temperature,nozzle diameter,and water subcooling promoted pressure build-up in the melt pool.However,a limited enhancement effect was observed when increasing the injection duration,which may be owing to the continually rising pressure in the interaction vessel or the isolation effect of the generated steam cavity.Regardless of whether a steam explosion occurred,the calculated mechanical and kinetic energy conversion efficiencies of the melt were relatively small(not exceeding 4.1%and 0.7%,respectively).Moreover,the range of the conversion efficiency was similar to that of previous water-droplet experiments,although the upper limit of the jet mode was slightly lower.展开更多
This study aimed to investigate the moment redistribution in continuous glass fiber reinforced polymer(GFRP)-concrete composite slabs caused by concrete cracking and steel bar yielding in the negative bending moment z...This study aimed to investigate the moment redistribution in continuous glass fiber reinforced polymer(GFRP)-concrete composite slabs caused by concrete cracking and steel bar yielding in the negative bending moment zone.An experimental bending moment redistribution test was conducted on continuous GFRP-concrete composite slabs,and a calculation method based on the conjugate beam method was proposed.The composite slabs were formed by combining GFRP profiles with a concrete layer and supported on steel beams to create two-span continuous composite slab specimens.Two methods,epoxy resin bonding,and stud connection,were used to connect the composite slabs with the steel beams.The experimental findings showed that the specimen connected with epoxy resin exhibited two moments redistribution phenomena during the loading process:concrete cracking and steel bar yielding at the internal support.In contrast,the composite slab connected with steel beams by studs exhibited only one-moment redistribution phenomenon throughout the loading process.As the concrete at the internal support cracked,the bending moment decreased in the internal support section and increased in the midspan section.When the steel bars yielded,the bending moment further decreased in the internal support section and increased in the mid-span section.Since GFRP profiles do not experience cracking,there was no significant decrease in the bending moment of the mid-span section.All test specimens experienced compressive failure of concrete at the mid-span section.Calculation results showed good agreement between the calculated and experimental values of bending moments in the mid-span section and internal support section.The proposed model can effectively predict the moment redistribution behavior of continuous GFRP-concrete composite slabs.展开更多
Based on simplified calculations of one-dimensional wave systems,loading pressure platform curves of Al-Cu gradient materials(GMs)impactor were designed.The Al-Cu GMs were prepared using tape-pressing sintering,and th...Based on simplified calculations of one-dimensional wave systems,loading pressure platform curves of Al-Cu gradient materials(GMs)impactor were designed.The Al-Cu GMs were prepared using tape-pressing sintering,and their acoustic properties were characterized to match the design path.The parallelism of the Al-Cu GM was confirmed using a three-dimensional surface profilometry machine.A one-stage light-gas gun was used to launch the Al-Cu GM,impacting an Al-LiF target at a velocity of 400 m/s.The results of the experimental strain rate demonstrate that the Al-Cu GMs can realize the precise control of the strain rate within the range of 10^(4)‒10^(5)/s in the high-speed impact experiments.展开更多
The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrat...The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrated with microfluidics,typically comprises barcode array,sample loading,and reaction unit array chips.Here,we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells,including protein biomarkers,microRNA(miRNA),circulating tumor DNA(ctDNA),single-cell secreted proteins,single-cell exosomes,and cell interactions.We begin with an overview of current high-throughput detection and analysis approaches.Following this,we outline recent improvements in microfluidic devices for biomolecule and single-cell detection,highlighting the benefits and limitations of these devices.This paper focuses on the research and development of microfluidic barcode biochips,covering their self-assembly substrate materials and their specific applications with biomolecules and single cells.Looking forward,we explore the prospects and challenges of this technology,with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies,and their large-scale commercialization.展开更多
In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial co...In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial communities,we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land,northern China.Through the use of high-throughput sequencing,we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration.Different slope positions were analyzed and spatial heterogeneity was assessed.The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration,and the following order was as follows:bottom slope>middle slope>crest slope.During the restoration in the Hulun Buir Sandy Land,the bacterial communities were dominated by Proteobacteria,Actinobacteria,and Acidobacteria,whereas the fungal communities were dominated by Ascomycota and Basidiomycota.Eutrophic bacterial abundance increased with the restoration duration,whereas oligotrophic bacterial and fungal abundance levels decreased.The soil bacterial abundance significantly increased with the increasing restoration duration,whereas the fungal diversity decreased after 11 a of restoration,except that at the crest slope.Redundancy analysis showed that pH,soil moisture content,total nitrogen,and vegetation-related factors affected the bacterial community structure(45.43%of the total variance explained).Canonical correspondence analysis indicated that pH,total phosphorus,and vegetation-related factors shaped the bacterial community structure(31.82%of the total variance explained).Structural equation modeling highlighted greater bacterial responses(R^(2)=0.49-0.79)to changes in environmental factors than those of fungi(R^(2)=0.20-0.48).The soil bacterial community was driven mainly by pH,soil moisture content,electrical conductivity,plant coverage,and litter dry weight.The abundance and diversity of the soil fungal community were mainly driven by plant coverage,litter dry weight,and herbaceous aboveground biomass,while there was no significant correlation between the soil fungal community structure and environmental factors.These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.展开更多
In this work,the selected icebreaker model experiment is performed in a towing tank.We focus on the influence of seawater salinity on ship ice resistance in the ice floe field and the innovative ice model and ship mod...In this work,the selected icebreaker model experiment is performed in a towing tank.We focus on the influence of seawater salinity on ship ice resistance in the ice floe field and the innovative ice model and ship model test technology,including the similitude rule of ship model tests,test principles,and validation with full-scale ship data.A formula for calculating the relationship between the temperature and salinity of the water is constructed,which can be used to simulate the role of seawater in freshwater ice pools.On this basis,the effect of salinity on the resistance of ships sailing in broken ice fields is studied.A technique in which artificial ice made of polyethylene spheres is used to simulate ice resistance is proposed.With a series of ship model experiments in spherical and triangular ice fields,the effects of salinity and velocity on the ice resistance test of the ship model are analyzed.A relationship of the ice resistance of the ship model to the spherical ice field and the triangular ice field is proposed.The conversion results are consistent with onsite data of the full-size ship,which verifies the method of converting the test results of the ship model to the prototype.展开更多
Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.H...Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.展开更多
The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectrosc...The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.展开更多
This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using s...This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.展开更多
基金supported by the National Natural Science Foundation of China(22372044,22393892,22002169,22071249)the Guangdong Basic and Applied Basic Research Foundation(2024A1515012583,2019A1515111111)the Major Program of Guangzhou National Laboratory(GZNL2023A02012)。
文摘Herein,we report a novel and highly efficient method for the synthesis ofα-phosphoryloxy carbonyl compounds via Rucatalyzed P(O)O–H insertion reactions of sulfoxonium ylides and phosphinic acids,with the assistance of high-throughput experimentation(HTE)and machine learning(ML).A variety of P(O)O−H derivatives,including diarylphosphates,alkyl phosphates,and alkoxyphosphates,are competent candidates to react with sulfoxonium ylides in this transformation,and variousα-phosphoryloxy carbonyls and propylene phosphates are directly constructed.This approach utilizes readily available sulfoxonium ylide as a carbene precursor,and features mild conditions,operational simplicity,and broad functional groups tolerance,and could be used for late-stage functionalization of structurally complex bioactive molecules.Moreover,a conducive exploration of the reaction space is also conducted(756 reactions)and a machine learning model for reaction yield prediction has been developed and applied,showcasing the practical application of this newly workflow(HTE-ML)in the field of synthetic chemistry.
基金supported by the Special Project of National Natural Science Foundation(42341204)the the National Natural Science Foundation of China(W2411009).
文摘The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.
基金supported by the National Key R&D Program of China(No.2022YFB3505700)the National Natural Science Foundation of China(No.51901079)+4 种基金Guangdong Science and Technology Program(No.2023A0505050145)the Natural Science Foundation of Guangdong Province(Nos.2024A1515030178,2020A1515010736 and 2021A1515010451)Guangzhou Municipal Science and Technology Program(No.202007020008)the Fundamental Research Funds for the Central Universities,the Opening Project of National Engineering Research Center for Powder Metallurgy of Titanium&Rare Metals,the Fundamental Research Funds for the Central Universities and Zhongshan Municipal Science and Technology Program(No.191007102629094)Zhongshan Collaborative Innovation Fund(No.2018C1001).
文摘Sm–Co-based films play an irreplaceable role in special applications due to their high curie temperature and magnetocrystalline anisotropic energy,especially in heat-assisted magnetic recording(HAMR),but the complex composition of Sm–Co phase and unclear synergistic coupling mechanisms of multi-elemental doping become the challenges to enhance the properties.In this work,a novel strategy combining magnetron sputtering and a high-throughput experiment method is applied to solve the above-mentioned problems.Fe/Cu co-doping highly increases the remanence while maintaining a coercivity larger than 26 kOe,leading to an enhancement of the magnetic energy product to 18.1 MGOe.X-ray diffraction(XRD)and high-resolution transmission electron microscope(HRTEM)reveals that SmCo_(5) phase occupies the major fraction,with Co atoms partially substituted by Fe and Cu atoms.In situ Lorentz transmission electron microscopy(LTEM)observations show that the Sm(Co,Cu)5 phase effectively prohibits domain wall motions,leading to an increase of coercivity(H_(c)).Fe doping increases the low saturation magnetization(M_(s))and low remanence(Mr)due to the Fe atom having a higher saturation magnetic moment.The magnetization reversal behaviors are further verified by micromagnetic simulations.Our results suggest that Sm–Co-based films prepared via Fe/Cu co-doping could be a promising candidate for high-performed HAMR in the future.
基金supported by the National Key Research and Development Program of China(No.2018YFB0704003)the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China(No.51820105001).
文摘The development of new engineering alloy chemistries and heat treatments is a time-consuming and iterative process.Here,a hybrid approach of the high-throughput precipitation simulations and decisive experiments is developed to optimize the composition and manipulate the microstructure of Al-Zn-Mg-Cu alloys to achieve the expected yield strength and elongation.For that purpose,a multi-class Kampmann-Wagner numerical(KWN)framework is established and the contributions to precipitation kinetics and strength from primary phases and precipitates formed before age hardening are introduced for the first time.The composition/process-structure-property relationship of Al-Zn-Mg-Cu alloys is pre-sented and discussed in detail.Coupled with thermodynamic calculations,two concentration-optimized Al-Zn-Mg-Cu alloys with expected high yield strength and long elongation are designed,prepared,and characterized.The excellent strength and elongation of the designed alloys and the good agreement between the measured and model-predicted mechanical properties for these two alloys underscores the remarkable predictive power of the presently developed material design strategy.This work establishes a novel material design strategy for rapidly exploring the compositional space and investigating the effects of composition and heat treatment on the microstructure and performance of ultrahigh strength Al alloys and other materials.
基金support by the National Key Research and Development Program of China(grant no.2018YFA0703600)the National Natural Science Foundation of China(grant no.51825104).
文摘Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.
基金financial supports from the National Key R&D Program of China (No.2023YFB3712400)National Natural Science Foundation of China (No.52371040)Joint Fund for Regional Innovation of Hunan Provincial Natural Science Foundation,China (No.2023JJ50333)。
文摘Ti alloys,as leading lightweight and high-strength metallic materials,exhibit significant application potential in aerospace,marine engineering,biomedical,and other industries.However,the lack of fundamental understanding of the microstructure−property relationship results in prolonged research and development(R&D)cycles,hindering the optimization of the performance of Ti alloys.Recently,the advent of high-throughput experimental(HTE)technology has shown promise in facilitating the efficient and demand-driven development of next-generation Ti alloys.This work reviews the latest advancements in HTE technology for Ti alloys.The high-throughput preparation(HTP)techniques commonly used in the fabrication of Ti alloys are addressed,including diffusion multiple,additive manufacturing(AM),vapor deposition and others.The current applications of high-throughput characterization(HTC)techniques in Ti alloys are shown.Finally,the research achievements in HTE technology for Ti alloys are summarized and the challenges faced in their industrial application are discussed.
基金The authors are grateful for financial support from the National Key Projects for Fundamental Research and Development of China(2021YFA1500803)the National Natural Science Foundation of China(51825205,52120105002,22102202,22088102,U22A20391)+1 种基金the DNL Cooperation Fund,CAS(DNL202016)the CAS Project for Young Scientists in Basic Research(YSBR-004).
文摘Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金United Arab Emirates University,Grant/Award Number:12R104 and 12R121。
文摘Cardiac injury initiates repair mechanisms and results in cardiac remodeling and fi-brosis,which appears to be a leading cause of cardiovascular diseases.Cardiac fi-brosis is characterized by the accumulation of extracellular matrix proteins,mainly collagen in the cardiac interstitium.Many experimental studies have demonstrated that fibrotic injury in the heart is reversible;therefore,it is vital to understand differ-ent molecular mechanisms that are involved in the initiation,progression,and resolu-tion of cardiac fibrosis to enable the development of antifibrotic agents.Of the many experimental models,one of the recent models that has gained renewed interest is isoproterenol(ISP)-induced cardiac fibrosis.ISP is a synthetic catecholamine,sympa-thomimetic,and nonselectiveβ-adrenergic receptor agonist.The overstimulated and sustained activation ofβ-adrenergic receptors has been reported to induce biochemi-cal and physiological alterations and ultimately result in cardiac remodeling.ISP has been used for decades to induce acute myocardial infarction.However,the use of low doses and chronic administration of ISP have been shown to induce cardiac fibrosis;this practice has increased in recent years.Intraperitoneal or subcutaneous ISP has been widely used in preclinical studies to induce cardiac remodeling manifested by fibrosis and hypertrophy.The induced oxidative stress with subsequent perturbations in cellular signaling cascades through triggering the release of free radicals is consid-ered the initiating mechanism of myocardial fibrosis.ISP is consistently used to induce fibrosis in laboratory animals and in cardiomyocytes isolated from animals.In recent years,numerous phytochemicals and synthetic molecules have been evaluated in ISP-induced cardiac fibrosis.The present review exclusively provides a comprehensive summary of the pathological biochemical,histological,and molecular mechanisms of ISP in inducing cardiac fibrosis and hypertrophy.It also summarizes the application of this experimental model in the therapeutic evaluation of natural as well as syn-thetic compounds to demonstrate their potential in mitigating myocardial fibrosis and hypertrophy.
文摘Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.
基金supported by Basic and Applied Basic research foundation of Guangdong province(Nos.2021A1515010343 and 2022A1515011582)the Science and Technology Program of Guangdong Province(Nos.2021A0505030026 and 2022A0505050029).
文摘In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-based alloy(typically pure lead or lead-bismuth eutectic(LBE))is used as the coolant.To clarify the pressure build-up characteristics under water-jet injection,this study conducted several experiments by injecting pressurized water into a molten LBE pool at Sun Yat-sen University.To obtain a further understanding,several new experimental parameters were adopted,including the melt temperature,water subcooling,injection pressure,injection duration,and nozzle diameter.Through detailed analyses,it was found that the pressure and temperature during the water-melt interaction exhibited a consistent variation trend with our previous water-droplet injection mode LBE experiment.Similarly,the existence of a steam explosion was confirmed,which typically results in a much stronger pressure build-up.For the non-explosion cases,increasing the injection pressure,melt-pool temperature,nozzle diameter,and water subcooling promoted pressure build-up in the melt pool.However,a limited enhancement effect was observed when increasing the injection duration,which may be owing to the continually rising pressure in the interaction vessel or the isolation effect of the generated steam cavity.Regardless of whether a steam explosion occurred,the calculated mechanical and kinetic energy conversion efficiencies of the melt were relatively small(not exceeding 4.1%and 0.7%,respectively).Moreover,the range of the conversion efficiency was similar to that of previous water-droplet experiments,although the upper limit of the jet mode was slightly lower.
基金supported by National Natural Science Foundation of China(Project No.51878156,received by Wen-Wei Wang) and EPC Innovation Consulting Project for Longkou Nanshan LNG Phase I Receiving Terminal(Z2000LGENT0399,received by Wen-Wei Wang and ZhaoJun Zhang).
文摘This study aimed to investigate the moment redistribution in continuous glass fiber reinforced polymer(GFRP)-concrete composite slabs caused by concrete cracking and steel bar yielding in the negative bending moment zone.An experimental bending moment redistribution test was conducted on continuous GFRP-concrete composite slabs,and a calculation method based on the conjugate beam method was proposed.The composite slabs were formed by combining GFRP profiles with a concrete layer and supported on steel beams to create two-span continuous composite slab specimens.Two methods,epoxy resin bonding,and stud connection,were used to connect the composite slabs with the steel beams.The experimental findings showed that the specimen connected with epoxy resin exhibited two moments redistribution phenomena during the loading process:concrete cracking and steel bar yielding at the internal support.In contrast,the composite slab connected with steel beams by studs exhibited only one-moment redistribution phenomenon throughout the loading process.As the concrete at the internal support cracked,the bending moment decreased in the internal support section and increased in the midspan section.When the steel bars yielded,the bending moment further decreased in the internal support section and increased in the mid-span section.Since GFRP profiles do not experience cracking,there was no significant decrease in the bending moment of the mid-span section.All test specimens experienced compressive failure of concrete at the mid-span section.Calculation results showed good agreement between the calculated and experimental values of bending moments in the mid-span section and internal support section.The proposed model can effectively predict the moment redistribution behavior of continuous GFRP-concrete composite slabs.
基金Natural Science Foundation of Hubei Province(2024AFB432)National Natural Science Foundation of China(52171045,12302436,52302095)Research Fund of Jianghan University(2023JCYJ05)。
文摘Based on simplified calculations of one-dimensional wave systems,loading pressure platform curves of Al-Cu gradient materials(GMs)impactor were designed.The Al-Cu GMs were prepared using tape-pressing sintering,and their acoustic properties were characterized to match the design path.The parallelism of the Al-Cu GM was confirmed using a three-dimensional surface profilometry machine.A one-stage light-gas gun was used to launch the Al-Cu GM,impacting an Al-LiF target at a velocity of 400 m/s.The results of the experimental strain rate demonstrate that the Al-Cu GMs can realize the precise control of the strain rate within the range of 10^(4)‒10^(5)/s in the high-speed impact experiments.
基金supported by the National Key Research and Development Plan of China(2023YFB3210400)the Natural Science Innovation Group Foundation of China(T2321004)+3 种基金the National Natural Science Foundation of China(62174101)Shandong University Integrated Research and Cultivation Project(2022JC001)Key Research and Development Plan of Shandong Province(Major Science and Technology Innovation Project2022CXGC020501).
文摘The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrated with microfluidics,typically comprises barcode array,sample loading,and reaction unit array chips.Here,we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells,including protein biomarkers,microRNA(miRNA),circulating tumor DNA(ctDNA),single-cell secreted proteins,single-cell exosomes,and cell interactions.We begin with an overview of current high-throughput detection and analysis approaches.Following this,we outline recent improvements in microfluidic devices for biomolecule and single-cell detection,highlighting the benefits and limitations of these devices.This paper focuses on the research and development of microfluidic barcode biochips,covering their self-assembly substrate materials and their specific applications with biomolecules and single cells.Looking forward,we explore the prospects and challenges of this technology,with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies,and their large-scale commercialization.
基金supported by the National Ecological Environment Survey and Assessment(2024-vertical-0107)the Fundamental Research Funds for the Central Public-interest Scientific Institution(2023YSKY-26)the Hulun Buir Grassland Ecological Restoration Comprehensive Survey Project(DD20230474).
文摘In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial communities,we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land,northern China.Through the use of high-throughput sequencing,we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration.Different slope positions were analyzed and spatial heterogeneity was assessed.The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration,and the following order was as follows:bottom slope>middle slope>crest slope.During the restoration in the Hulun Buir Sandy Land,the bacterial communities were dominated by Proteobacteria,Actinobacteria,and Acidobacteria,whereas the fungal communities were dominated by Ascomycota and Basidiomycota.Eutrophic bacterial abundance increased with the restoration duration,whereas oligotrophic bacterial and fungal abundance levels decreased.The soil bacterial abundance significantly increased with the increasing restoration duration,whereas the fungal diversity decreased after 11 a of restoration,except that at the crest slope.Redundancy analysis showed that pH,soil moisture content,total nitrogen,and vegetation-related factors affected the bacterial community structure(45.43%of the total variance explained).Canonical correspondence analysis indicated that pH,total phosphorus,and vegetation-related factors shaped the bacterial community structure(31.82%of the total variance explained).Structural equation modeling highlighted greater bacterial responses(R^(2)=0.49-0.79)to changes in environmental factors than those of fungi(R^(2)=0.20-0.48).The soil bacterial community was driven mainly by pH,soil moisture content,electrical conductivity,plant coverage,and litter dry weight.The abundance and diversity of the soil fungal community were mainly driven by plant coverage,litter dry weight,and herbaceous aboveground biomass,while there was no significant correlation between the soil fungal community structure and environmental factors.These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.
基金financially supported by Jiangsu Province University(High Tech Ship)Collaborative Innovation Center(Grant No.XTCXKY20230008).
文摘In this work,the selected icebreaker model experiment is performed in a towing tank.We focus on the influence of seawater salinity on ship ice resistance in the ice floe field and the innovative ice model and ship model test technology,including the similitude rule of ship model tests,test principles,and validation with full-scale ship data.A formula for calculating the relationship between the temperature and salinity of the water is constructed,which can be used to simulate the role of seawater in freshwater ice pools.On this basis,the effect of salinity on the resistance of ships sailing in broken ice fields is studied.A technique in which artificial ice made of polyethylene spheres is used to simulate ice resistance is proposed.With a series of ship model experiments in spherical and triangular ice fields,the effects of salinity and velocity on the ice resistance test of the ship model are analyzed.A relationship of the ice resistance of the ship model to the spherical ice field and the triangular ice field is proposed.The conversion results are consistent with onsite data of the full-size ship,which verifies the method of converting the test results of the ship model to the prototype.
基金supported by the U.S.Army Research Laboratory through their award#W911NF-22-2-0040the Ministry of Education,Youth and Sports of the Czech Republic through the e-INFRA CZ(ID:90254).
文摘Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(Grant number:RS-2025-02316700,and RS-2025-00522430)the China Scholarship Council Program。
文摘The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.
基金The financial support provided by the Project of the National Natural Science Foundation of China (22308314,U22A20415)the Natural Science Foundation of Zhejiang Province (LQ24B060001)+1 种基金the "Pioneer" and "Leading Goose" Research & Development Program of Zhejiang (2022C01SA442617)the SINOPEC Technology Development Project (224244)
文摘This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.
